NDLI: Wavelet-based extreme learning machine for distribution grid fault location

Content Provider	IET Digital Library
Author	Shafiullah, Md Abido, Mohammad A. Hamouz, Zakariya Al
Abstract	Precise knowledge of faults is very exigent to reduce the outage duration as most of the customer minute losses in distribution grids occur due to longer period of interruptions caused by faults. This study proposes a fault location technique combining advanced signal processing and machine learning tools for distribution grids. The proposed technique decomposes three-phase currents measured from sending end employing wavelet transform (WT) and collects useful features to fetch them as inputs of extreme learning machine (ELM). Satisfactory values of the selected statistical performance measures validate the efficacy of proposed fault location technique. Besides, the efficacy of support vector regression (SVR) and artificial neural network (ANN) are also tested employing the WT extracted features. The presented results show the superiority of ELM-WT technique over SVR-WT and ANN-WT techniques in terms of the selected performance measures and training times. Additionally, the proposed technique is independent of fault resistance, inception angle, the presence of measurement noise, thermal expansion/contraction of the distribution line and pre-fault loading condition. Furthermore, the hybrid method detects and classifies different types of faults before locating them with different machine learning tools.
Starting Page	4256
Ending Page	4263
Page Count	8
ISSN	17518687
Volume Number	11
e-ISSN	17518695
Issue Number	Issue 17, Nov (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-gtd/11/17
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2017.0656
Journal	IET Generation, Transmission & Distribution
Publisher Date	2017-07-24
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	ANN Artificial Neural Network Distribution Grid Fault Location Distribution Line Distribution Network Fault Location Fault Resistance Inception Angle Integral Transforms in Numerical Analysis Knowledge Engineering Technique Learning in AI Machine Learning Tool Measurement Noise Neural Computing Technique Neural Nets Power Distribution Fault Power Grid Power System Measurement And Metering Pre-fault Loading Condition Regression Analysis Signal Processing Statistical Performance Statistics Support Vector Machine Support Vector Regression SVR Thermal Contraction Thermal Expansion Three-phase Current Measurement Wavelet Transform Wavelet-based Extreme Learning Machine
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Energy Engineering and Power Technology Electrical and Electronic Engineering

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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